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- W2366366948 abstract "Support vector machine(SVM) based on statistical learning theory is a powerful tool for pattern recognition problems.However,since the training algorithm is too complex to accept large quantitise of trainging data,the use for speaker recognition is limited.A system based on Gaussian mixture model(GMM) statistical parameters and SVM for text-independent speaker identification is proposed to aim at this problem.The SVM speaker model is trained from the parameters of GMM,which is an efficient method on large-scale training data.The results of experiments showed that the system has good performance.Especially when combined with cepstral coefficient weighting method,the system's robustness and performance are further improved." @default.
- W2366366948 created "2016-06-24" @default.
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- W2366366948 date "2006-01-01" @default.
- W2366366948 modified "2023-09-26" @default.
- W2366366948 title "Text-Independent Speaker Identification Based on GMM Statistical Parameters and SVM" @default.
- W2366366948 hasPublicationYear "2006" @default.
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